Building Peak Load Management With High Resolution Weather Data

2015 ◽  
Author(s):  
Yehisson Tibana ◽  
Estatio Gutierrez ◽  
M. Arend ◽  
J. E. Gonzalez
Keyword(s):  
High Resolution ◽  
Weather Forecasting ◽  
Peak Load ◽  
Building Energy ◽  
Energy Model ◽  
Department Of Energy ◽  
Urban Heat Islands ◽  
Weather Data ◽  
Load Management ◽  
Peak Loads

Dense urban environments are exposed to the combined effects of rising global temperatures and urban heat islands. This combination is resulting in increasing trends of energy consumption in cities, associated mostly with air conditioning to maintain indoor human comfort conditions. During periods of extreme summer weather, electrical usage usually reaches peak loads, stressing the electrical grid. The purpose of this study is to explore the use of available, high resolution weather data by effectively preparing a building for peak load management. The subject of study is a 14 floor, 620,782 sq ft building located in uptown Manhattan, New York City (40.819257 N, −73.949288 W). To precisely quantify thermal loads of the buildings for the summer conditions; a single building energy model (SBEM), the US Department of Energy EnergyPlus™ was used. The SBEM was driven by a weather file built from weather data of the urbanized weather forecasting model (uWRF), a high resolution weather model coupled to a building energy model. The SBEM configuration and simulations were calibrated with winter actual gas and electricity data using 2010 as the benchmark year. In order to show the building peak load management, demand response techniques and technologies were implemented. The methods used to prepare the building included generator usage during high peak loads and use of a thermal storage system. An ensemble of cases was analyzed using current practice, use of high resolution weather data, and use of building preparation technologies. Results indicated an average summer peak savings of more than 30% with high resolution weather data.

scholarly journals Learning-Based Predictive Building Energy Model Using Weather Forecasts for Optimal Control of Domestic Energy Systems

2018 ◽  
Vol 11 (1) ◽  
pp. 147 ◽  
Author(s):  
Byung-Ki Jeon ◽  
Eui-Jong Kim ◽  
Younggy Shin ◽  
Kyoung-Ho Lee
Keyword(s):  
Predictive Control ◽  
Prediction Models ◽  
Weather Forecasting ◽  
Reference Model ◽  
Weather Prediction ◽  
Building Energy ◽  
Energy Model ◽  
Weather Data ◽  
Rc Building ◽  
Mean Square Errors

The aim of this study is to develop a model that can accurately calculate building loads and demand for predictive control. Thus, the building energy model needs to be combined with weather prediction models operated by a model predictive controller to forecast indoor temperatures for specified rates of supplied energy. In this study, a resistance–capacitance (RC) building model is proposed where the parameters of the models are determined by learning. Particle swarm optimization is used as a learning scheme to search for the optimal parameters. Weather prediction models are proposed that use a limited amount of forecasting information fed by local meteorological centers. Assuming that weather forecasting was perfect, hourly outdoor temperatures were accurately predicted; meanwhile, differences were observed in the predicted solar irradiances values. In investigations to verify the proposed method, a seven-resistance, five-capacitance (7R5C) model was tested against a reference model in EnergyPlus using the predicted weather data. The root-mean-square errors of the 7R5C model in the prediction of indoor temperatures on all the specified days were within 0.5 °C when learning was performed using reference data obtained from the previous five days and weather prediction was included. This level of deviation in predictive control is acceptable considering the magnitudes of the loads and demand of the tested building.

Forecasting Building Energy Demands for New York City With a Coupled Weather-Building Energy Model

2016 ◽  
Author(s):  
Luis E. Ortiz ◽  
Jorge E. Gonzalez ◽  
Estatio Gutierrez ◽  
Mark Arend ◽  
Thomas Legbandt ◽  
...  
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Peak Load ◽  
Building Energy ◽  
Urban Morphology ◽  
Energy Model ◽  
Peak Loads ◽  
Extreme Heat Event ◽  
The City

Major metropolitan centers experience challenges during management of peak electrical loads, typically occurring during extreme summer events. These peak loads expose the reliability of the electrical grid and customers may incur in additional charges for peak load management in regulated demand-response markets. This opens the need for the development of analytical tools that can inform building managers and utilities about near future conditions so they are better able to avoid peak demand charges, reducing building operational costs. In this article, we report on a tool and methodology to forecast peak loads at the City Scale using New York City (NYC) as a test case. The city of New York experiences peak electric demand loads that reach up to 11 GW during the summertime, and are projected to increase to over 12 GW by 2025, as reported by the New York Independent System Operator (NYISO). The forecast is based on the Weather Research and Forecast model version 3.5, coupled with a building environment parameterization and building energy model. Urban morphology parameters are assimilated from the New York Primary Land Use Tax Lot Output (PLUTO), while the weather component of the model is initialized daily from the North American Mesoscale (NAM) model. A city-scale analysis is centered in the summer months of June-July 2015 which included an extreme heat event (i.e. heat wave). The 24-hr city-scale weather and energy forecasts show good agreement with the archived data from both weather stations records and energy records by NYISO.

scholarly journals The Vertical City Weather Generator (VCWG v1.0.0)

2019 ◽  
Author(s):  
Mohsen Moradi ◽  
Benjamin Dyer ◽  
Amir Nazem ◽  
Manoj K. Nambiar ◽  
M. Rafsan Nahian ◽  
...  
Keyword(s):  
Wind Speed ◽  
Potential Temperature ◽  
Building Energy ◽  
Energy Model ◽  
Weather Generator ◽  
Specific Humidity ◽  
Weather Data ◽  
Rural Site ◽  
Vertical Diffusion ◽  
Urban Microclimate

Abstract. The Vertical City Weather Generator (VCWG) is a computationally efficient urban microclimate model developed to predict temporal and vertical variation of temperature, wind speed, and specific humidity. It is composed of various sub models: a rural model, an urban microclimate model, and a building energy model. In a nearby rural site, a rural model is forced with weather data to solve a vertical diffusion equation to calculate vertical potential temperature profiles using a novel parameterization. The rural model also calculates a horizontal pressure gradient. The rural model outputs are then forced on a vertical diffusion urban microclimate model that solves vertical transport equations for momentum, temperature, and specific humidity. The urban microclimate model is also coupled to a building energy model using feedback interaction. The aerodynamic and thermal effects of urban elements and vegetation are considered in VCWG. To evaluate the VCWG model, a microclimate field campaign was held in Guelph, Canada, from 15 July 2018 to 5 September 2018. The meteorological measurements were carried out under a comprehensive set of wind directions, wind speeds, and thermal stability conditions in both the rural and the nearby urban areas. The model evaluation indicated that the VCWG predicted vertical profiles of meteorological variables in reasonable agreement with field measurements for selected days. In comparison to measurements, the overall model biases for potential temperature, wind speed, and specific humidity were within 5 %, 11 %, and 7 %, respectively. The performance of the model was further explored to investigate the effects of urban configurations such as plan and frontal area densities, varying levels of vegetation, seasonal variations, different climate zones, and time series analysis on the model predictions. The results obtained from the explorations were reasonably consistent with previous studies in the literature, justifying the reliability and computational efficiency of VCWG for operational urban development projects.

Forecasting Building Energy Demands With a Coupled Weather-Building Energy Model in a Dense Urban Environment

2016 ◽  
Vol 139 (1) ◽  
Author(s):  
Luis E. Ortiz ◽  
Jorge E. Gonzalez ◽  
Estatio Gutierrez ◽  
Mark Arend
Keyword(s):  
New York ◽  
Energy Demand ◽  
Building Energy ◽  
Urban Morphology ◽  
Energy Model ◽  
Demand Model ◽  
Peak Demand ◽  
Peak Loads ◽  
Extreme Heat Event ◽  
The City

Major new metropolitan centers experience challenges during management of peak electrical loads, typically occurring during extreme summer events. These peak loads expose the reliability of the electrical grid on the production and transmission side, while customers may incur considerable charges from increased metered peak demand, failing to meet demand response program obligations, or both. These challenges create a need for analytical tools that can inform building managers and utilities about near future conditions so they are better able to avoid peak demand charges and reduce building operational costs. In this article, we report on a tool and methodology to forecast peak loads at the city scale using New York City (NYC) as a test case. The city of New York experiences peak electric demand loads that reach up to 11 GW during the summertime, and are projected to increase to over 12 GW by 2025, as reported by the New York Independent System Operator (NYISO). The energy forecast is based on the Weather Research and Forecast (WRF) model version 3.5, coupled with a multilayer building energy model (BEM). Urban morphology parameters are assimilated from the New York Primary Land Use Tax-Lot Output (PLUTO), while the weather component of the model is initialized daily from the North American Mesoscale (NAM) model. A city-scale analysis is centered in the summer months of June–July 2015 which included an extreme heat event (i.e., heat wave). The 24-h city-scale weather and energy forecasts show good agreement with the archived data from both weather stations records and energy records by NYISO. This work also presents an exploration of space cooling savings from the use of white roofs as an application of the city-scale energy demand model.

scholarly journals On the Spatio-Temporal End-User Energy Demands of a Dense Urban Environment

2017 ◽  
Vol 139 (4) ◽  
Author(s):  
Krarti Ahmed ◽  
Luis E. Ortiz ◽  
J. E. González
Keyword(s):  
New York ◽  
Energy Demand ◽  
Demand Forecasting ◽  
Warm Season ◽  
Weather Forecast ◽  
Building Energy ◽  
Energy Model ◽  
Simulation Software ◽  
Weather Data ◽  
The City

Buildings in major metropolitan centers face increased peak electrical load during the warm season, especially during extreme heat events. City-wide, the increased demand for space cooling can stress the grid, increasing generation costs. It is therefore imperative to better understand building energy consumption profiles at the city scale. This understanding is not only paramount for users to avoid peak demand charges but also for utilities to improve load management. This study aims to develop a city-scale energy demand forecasting tool using high resolution weather data interfaced with a single building energy model. The forecasting tool was tested in New York City (NYC) due to the availability of building morphology data. We identified 51 building archetypes, based on the building function (residential, educational, or office), the age of the building, and the land use type. The single building simulation software used is energyplus which was coupled to an urbanized weather research and forecasting (uWRF) model for weather forecast input. Individual buildings were linked to the archetypes and scaled using the building total floor area. The single building energy model is coupled to the weather model resulting in energy maps of the city. These maps provide an energy end-use profile for NYC for total and individual components including lighting, equipment and heating, ventilation, and air-conditioning (HVAC). The methodology was validated with single building energy data for a particular location, and with city-scale electric load archives, showing good agreements in both cases.

Prediction, Testing, and Analysis of a 50 m Long Pile in Soft Marine Clay

2019 ◽  
Vol 13 (2) ◽  
Author(s):  
Bengt Fellenius
Keyword(s):  
Peak Load ◽  
Pile Head ◽  
Marine Clay ◽  
Loading Test ◽  
Head Displacement ◽  
Floating Pile ◽  
Peak Loads ◽  
Static Loading Test ◽  
Beta Coefficient ◽  
Load Movement

On April 4, 2018, 209 days after driving, a static loading test was performed on a 50 m long, strain-gage instrumented, square 275-mm diameter, precast, shaft-bearing (“floating”) pile in Göteborg, Sweden. The soil profile consisted of a 90 m thick, soft, postglacial, marine clay. The groundwater table was at about 1.0 m depth. The undrained shear strength was about 20 kPa at 10 m depth and increased linearly to about 80 kPa at 55m depth. The load-distribution at the peak load correlated to an average effective stress beta-coefficient of 0.19 along the pile or, alternatively, a unit shaft shear resistance of 15 kPa at 10 m depth increasing to about 65 kPa at 50 m depth, indicating an α-coefficient of about 0.80. Prior to the test, geotechnical engineers around the world were invited to predict the load-movement curve to be established in the test—22 predictions from 10 countries were received. The predictions of pile stiffness, and pile head displacement showed considerable scatter, however. Predicted peak loads ranged from 65% to 200% of the actual 1,800-kN peak-load, and 35% to 300% of the load at 22-mm movement.

scholarly journals Utilising Open Geospatial Data to Refine Weather Variables for Building Energy Performance Evaluation—Incident Solar Radiation and Wind-Driven Infiltration Modelling

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 802
Author(s):  
Kristian Skeie ◽  
Arild Gustavsen
Keyword(s):  
Solar Radiation ◽  
Web Service ◽  
Meteorological Data ◽  
Surface Wind ◽  
Building Energy ◽  
Energy Performance ◽  
Geospatial Data ◽  
Weather Data ◽  
Building Site ◽  
Incident Solar Radiation

In building thermal energy characterisation, the relevance of proper modelling of the effects caused by solar radiation, temperature and wind is seen as a critical factor. Open geospatial datasets are growing in diversity, easing access to meteorological data and other relevant information that can be used for building energy modelling. However, the application of geospatial techniques combining multiple open datasets is not yet common in the often scripted workflows of data-driven building thermal performance characterisation. We present a method for processing time-series from climate reanalysis and satellite-derived solar irradiance services, by implementing land-use, and elevation raster maps served in an elevation profile web-service. The article describes a methodology to: (1) adapt gridded weather data to four case-building sites in Europe; (2) calculate the incident solar radiation on the building facades; (3) estimate wind and temperature-dependent infiltration using a single-zone infiltration model and (4) including separating and evaluating the sheltering effect of buildings and trees in the vicinity, based on building footprints. Calculations of solar radiation, surface wind and air infiltration potential are done using validated models published in the scientific literature. We found that using scripting tools to automate geoprocessing tasks is widespread, and implementing such techniques in conjunction with an elevation profile web service made it possible to utilise information from open geospatial data surrounding a building site effectively. We expect that the modelling approach could be further improved, including diffuse-shading methods and evaluating other wind shelter methods for urban settings.

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